Methods and systems for using multiple data sets to analyze performance metrics of targeted companies

ABSTRACT

New and improved methods and systems for modeling the performance of selected company metrics. Multiple, nontraditional sets of objective data along with mathematical analytical techniques are used to provide transparency and visibility into company performance relating to the particular metrics. Company inflection points and changes in strategy may be identified. The performance of a company and/or the performance of a selected industry or industry sector may be analyzed.

FIELD OF THE INVENTION

The present invention relates generally to methods and systems foranalyzing the performance of companies and more particularly to methodsand systems for using multiple data sets to analyze selected performancemetrics of selected companies.

BACKGROUND OF THE INVENTION

There are several types of analysts on Wall Street today, each producingdifferent types of reports for different kinds of clients. Onewell-known type of analyst is the sell-side analyst who producessell-side research. Sell-side analysts are employed by brokerage housesto analyze specific companies and write in-depth research reports,conducting what is sometimes called primary research. These reports areused to “sell” an idea to individuals and institutional clients.Individual investors can gain access to these reports by having accountswith the brokerage firm that generates them. For example, to getresearch from Merrill Lynch, one need have an account with a MerrillLynch broker. Sometimes the reports can be purchased through a thirdparty. Institutional clients such as mutual fund managers get researchfrom a brokerage's institutional brokers.

A typical sell-side research report contains a detailed analysis of acompany's competitive advantages and provides information on companymanagement expertise and how the company's operating and stock valuationcompares to a peer group and its industry. The typical report alsocontains an earnings model and states the assumptions that are used tocreate the forecast. Sell-side reports are updated on a regular basis asnew information becomes available. Further, sell-side analysts generallymake themselves personally available to meet and talk directly withbuy-side analysts and potential buyers.

Information for sell-side reports may be obtained by reading thecompany's SEC filings, meeting with its management, and, if possible,talking with its suppliers and customers. Research may also entailanalyzing the company's publicly-traded peers for the purpose of betterunderstanding differences in operating results and stock valuations.This latter approach is called fundamental analysis because it focuseson the company's fundamentals. Such research can be a time-consumingprocess that limits a typical sell-side analyst to specializing in asmall number of industries and covering a small group of companieswithin those industries. The content and nature of sell-side reports maybe limited by government SEC regulations.

A second type of analyst is the buy-side analyst. Buy-side analysts areemployed by fund managers and pension funds. Like sell-side analysis, abuy-side analyst specializes in a few sectors and analyzes stocks tomake buy/sell recommendations. Buy-side researchers typically differfrom sell-side researchers in various respects: they follow more stocks(30-40), they write very brief reports (generally one or two pages), andtheir research is only distributed to fund managers—not to sell-sideanalysts or to investors at large. Further, while sell-side analysts arelimited to reporting on companies that their brokerage represents,buy-side analysts are not thus limited. In fact, buy-side analystsconstantly work to identify and report on companies that they expect tobe of current interest to their customers. Readers will understand thatdifferent buy-side analysts use different criteria for identifying ‘hot’companies.

A buy-side analyst can cover more stocks than a sell-side analystbecause they have access to more information, including sell-sideresearch. They also have the opportunity to attend industry conferences,hosted by sell-side firms.

While company fundamentals are widely accepted indicators of a company'sperformance, fundamentals of publicly traded companies are generallyavailable to everyone. Much of the added value of buy- and sell-sideresearch comes from the ‘other’ data included in such research. In someinstances, commercially available, analytical data is used by analystsas part of their reports. For example, Nielsen™ provides data relatingto consumer audiences in the interne, media and entertainmentindustries. NPD provides point of sale data in the video game industryas well as data relating to food services, apparel and appliances. IDCprovides data relating to the information technology andtelecommunications industries, while IRI provides data relating topoint-of-sales activities in various industries including consumergoods. Yet another source of data comprises the industry standardpractice of collecting data by performing deep, automated searches ofpublicly available data sets, such as those available on the Internet.These and other well-known data sources are available to analysts indeveloping buy- and sell-side reports. While such data is useful andinteresting in certain respects, it also has certain drawbacks. Forexample, such analytical data sources are relatively limited in number.The data available from such sources is generally determined by thecollector as pertinent to an industry and may not be particularly usefulwith respect to any particular company within that industry. An analystmay be lucky to find one set of data having limited relevance to aparticular company of interest.

Beyond fundamental analysis and the use of commercial analytical data,the remainder of data included in most analyst reports tends to beanecdotally based and/or quite subjective to the personal opinions ofboth the analysts and users. This means that, despite analysts' bestefforts, much buy- and sell-side research suffers from the disadvantagesof being anecdotal and subjective.

Because significant investments are made based upon analyst reportscontaining, in large part, anecdotal and subjective data, reliance uponsuch reports exposes the consumers of such data to risks and theproviders of such data to potential liability. The present inventorshave determined that it would be highly desirable to develop new ways ofanalyzing company performance, in addition to fundamental analysis, thatis based on objective mathematical analysis. Such analysis woulddesirably be based on proven and repeatable objective principals andwould not be subject to the vagaries, inconsistencies andsubjective-ness of existing buy- and sell-side analysis.

SUMMARY OF THE INVENTION

In accordance with an embodiment of the present invention there areprovided methods and systems for using multiple sources of pertinent,measurable data to analyze selected performance metrics of selectedcompanies. Such data, while not fundamental performance data, canprovide significant, objective insight into the current and futureperformance of relevant company metrics. Company metrics which may beanalyzed using the current invention are not limited to financialperformance, but include many other measures of interest to investors asdescribed below. The use of multiple data sources enables the analyst tomathematically interpret the information developed using the variousdata sets to improve the accuracy of the analyses. Analyses provided bythe present invention thus provides transparency into the activities andperformance of a selected company, including but not limited to, invarious instances: provide the ability to evaluate past companyperformance intelligently, identify strategic and tactical shifts in acompany's performance and in some instances to forecast a company'sfuture performance.

In accordance with the invention there are provided methods and systemsfor preparing a model to analyze a performance metric of a selectedcompany, one exemplary method comprising: identifying a first datasource having a first set of data pertinent to the performance metric ofthe selected company; collecting into the computer the first set ofdata; validating the first set of data; identifying an additional datasource, the additional data source having an additional set of datadifferent from the first set of data and pertinent to the performancemetric of the selected company; collecting into the computer theadditional set of data; validating the additional set of data; combiningthe first set of data and the additional set of data in a combined dataset; selecting an analytical process to apply to the combined data set;applying the selected analytical process to the combined data set todevelop a model of the performance metric; and validating the model ofthe performance metric against the actual performance of the selectedcompany.

The present invention has the significant advantage of providingentirely new sources of objective analysis for company reports. Becausedata can be obtained from a vast selection of sources, more varied andreliable analysis of company performance can be provided to users. Usersneed no longer make significant investment decisions based heavily uponlimited and/or anecdotal data and subjective interpretation by analysts.

DESCRIPTION OF THE DRAWING FIGURES

These and other objects, features and advantages of the invention willnow become apparent from a consideration of the detailed description ofthe invention as set out below when read in conjunction with the drawingFigures, in which:

FIG. 1 is a block diagram of a system constructed in accordance with thepresent invention;

FIG. 2 is a flow chart showing a process for analyzing the performanceof a company in accordance with the present invention;

FIG. 3 is a flow chart showing the details of the data validationprocess of FIG. 2; and

FIG. 4 is a flow chart showing the details of the data analysis processof FIG. 2.

DETAILED DESCRIPTION OF THE INVENTION

There will now be shown and described new and improved processes andsystems for analyzing the performance of a company. As used here, theterm “performance of a company” refers to any metric of a companyselected by an analyst as of interest to an investor. Performance maythus include, metrics relating to: financial performance, customer base,product set, cost of goods, cost of advertising, geographicalactivities, and other metrics of interest to investors in a company.Unless expressly stated otherwise, examples and lists of alternatives asused herein are without limitation.

As will be shown below, the present invention uses multiple,non-traditional data sets to provide transparency of or visibility intothe selected performance metric of the company. Because objective datasets are used in the evaluation of a company's performance, theresulting analysis is generally objective and mathematically repeatablein comparison to the subjective or anecdotal evidence often used by buy-and sell-side analysts. Further, because multiple data sets are used toperform the analysis, the accuracy of the analysis is significantlyimproved as described herein below.

With reference now to FIG. 1, there is shown a system 100 including ananalytical system 102 operative in accordance with the processesdescribed below to analyze the performance of selected metrics ofselected companies. Analytical system 102 is seen to include a processor102A connected to a database 102B and an operator terminal 102C. In oneexemplary embodiment, processor 102A includes an interim microprocessoroperated by a Microsoft™ Windows™ operating system. Database 102Bcomprises an appropriate combination of memory elements, for example acombination of magnetic, optical and semiconductor memory elements.Terminal 102C comprises a conventional keyboard and display screen.While one exemplary system has been described, numerous other systemsincluding combinations of processors, operating systems, memory,software, databases and operator terminals will now be apparent to thereader, which are capable of performing the invention as describedherein.

Processor 102A is seen connected through an appropriate communicationschannel 104A to a traditional data source 106 for an identified company,such as those data sources described herein above, as well as to asource of fundamental data 108 for the same company. The processor issimilarly connected through an appropriate communications channel 104Bto a plurality of non-traditional data sources indicated at 110A, B, N.Communications channels 104A, B can comprise, for example, telephone,facsimile, mail and/or public or private network connections of typeswell known to the reader. Traditional data source 106 comprises one ormore commercial data suppliers as are described above, while companyfundamental data source 108 may comprise the company under analysisitself, or other well-known sources of fundamental data. Exemplarysources of non-traditional data 110A-N are described in detail hereinbelow.

With reference now to FIG. 2, a process 200 is shown for analyzing datato evaluate and model one or more performance metrics of one or moreselected companies. In the exemplary embodiment, the processes describedherein are performed by analytical system 102, processor 102A operating,for example, commercially available computer programs such as:Microsoft™ SQL Server, The Mathworks™ MATLAB software including thestatistical analysis, neural network and spectral analysis modules,Microsoft™ Data Analyzer and other data handling and analysis programsas are known in the art.

Initially it is necessary to identify a selected company and theperformance metric or metrics to be analyzed for that company (step205). For purposes of illustrating the present invention, process 200will initially be described with respect to the analysis of theperformance of a fictional, publicly traded, national used cardealership, referred to herein as Autostore. Potential investors inAutostore would like more information about the company. These investorshave access to publicly available data from traditional data sources 106and fundamental data source 108, in this case company SEC reportsavailable from many commercial sources. However, the investors wouldlike further insight into the operation and performance of Autostoreprior to making an investment decision. An analyst operating analyticalsystem 102 determines that the company performance metric of unit sales,in this example unit auto sales, would be of value to the potentialinvestors and determines to operate analytical system 102 to providevisibility into that aspect of Autostore's performance.

With continuing reference to FIG. 2, exploration is performed toidentify a first source of data likely to provide insight intoAutostore's unit sales (step 210). In this instance, it is determinedthat a first data source, a source of multi-state automobileregistration data, is available through licensing of the data from agovernmental agency for use by analytical system 102. The data setincludes, for each state, demographic data on each automobile'sregistrant, year and model data on each automobile and geographic dataon the location of each registered automobile. In accordance with thepresent invention, this first data set comprises certaincharacteristics: i) it is not fundamental company data, ii) while notfundamental data, it is objective and not anecdotal in nature, and iii)it is available for use, in this case through licensing, by analyticalsystem 102. Of particular interest in the licensed data set is thenumber of monthly title registrations for Autostore, available for aperiod of 8 historical quarters, that is, eight, 3-month historicalperiods.

The data is licensed and imported into analytical system 102 (step 215)and its viability is evaluated (step 220). More particularly, it isknown that no data set is likely to be without problems, such ascompleteness and accuracy, which may impact its use. With reference nowto FIG. 3, the viability of the data set is determined by first cleaningand filtering the data to remove obvious bad and/or incomplete dataentries or other data entries that can be determined to be inappropriatein their content (step 305). The data is then analyzed (step 310) todetermine its validity (step 315).

In the present example such analysis includes evaluation of thecompleteness of the data across all relevant geographies and thecompleteness of the entries within the particular registration fields ofinterest. As noted above, the data to be used relates to registrationsarising from unit auto sales by Autostore, particularly the date andseller fields by which Autostore registrations are determined. Furtherin the current example, it is determined that the registration data, andparticularly the registration dates, suffer a latency error caused bythe lag between the purchase date and the registration date for atypical automobile sale. This latency error is corrected, for exampleusing survey data to determine an average latency period. Optionally,more complex and sophisticated cleaning can be done, for exampleadjusting the latency differently for different dates, geographies,types of vehicles and such other factors as may be determined andcorrected. It will be understood that the process of determining theviability of a data set will vary from data set to data set. However, ineach case the process will include cleaning the data, as well asdetermining the overall fitness of the data for the intended purpose.Usability factors will vary from data set to data set, but will likelyinclude such parameters as accuracy, completeness, integrity,‘cleanliness’ and other factors going to its validity, but not itsusefulness for analysis of unit sales, which is determined below.

With reference back to FIG. 2, if the data is not viable (step 225) itis discarded (step 230) and the process re-started from step 210. If thedata is viable (step 225) and comprises the first data set to be used togenerate the metric model (step 240), then it is processed with the goalof using it to analyze, through modeling, the selected performancemetric of the selected company (step 250), in this case the unit salesof Autostore. With reference to FIG. 4, an analysis and modeling of theselected data includes first selecting the data to be used (step 405).In the present instance, much data is available in the vehicleregistration database. As noted above, the registration date and sellerdata is selected for use, preferably cleaned and validated in the mannerdescribed above.

Next there is selected a mathematical analytical technique to be appliedto the data (step 410) with which to construct a model for thetranslation of data as a proxy for the metric of interest. It will beunderstood by the reader that numerous analytical techniques can be usedto analyze the date and seller data of registered vehicles whereby tomodel the unit sales of Autostore. The invention contemplates the use ofvarious analytical techniques including, but not limited to, linearregression analysis, multivariate (nonlinear) regression analysis, timeseries analysis, smoothing methods, spectral analysis, neural networks,artificial intelligence and machine learning as well as a myriad ofother analytical and predictive techniques as will now be apparent tothe reader.

It will be understood by the reader that neural networks comprisecommercially available, artificial intelligence models that operate byattempting to imitate the way a human brain works. Rather than using adigital model, in which all computations manipulate zeros and ones, aneural network works by creating connections between processingelements, the computer equivalent of neurons. The organization andweights of the connections determine the output. Neural networks areunderstood to be particularly effective for predicting events when thenetworks have a large database of data to draw upon. Numerous,commercially available neural network software packages are availableincluding, but not limited to: neuralware™, Siebel™, microstrategy™ andothers known to the reader. Numerous other commercially availablesoftware, examples of which are named above, are similarly available toperform the described regression and spectral analyses.

With reference back to FIG. 2, because the correspondence betweenregistration data and unit sales is anticipated to be relatively strong,the system operator may begin with a linear regression analysis. If thisis the first data set used for the analysis (step 240) then the processcontinues with step 250 wherein a mathematical model is prepared basedupon the previously described mathematical processing. The reader willunderstand that the model comprises a mathematical formula arising fromthe analytical technique applied to the selected data, in the describedexample a mathematical formula arising from the linear regressionanalysis.

The developed model is then compared to the actual performance of thecompany (step 255) to determine its accuracy. Such comparison may be tothe historical, current and future performance of the company, typicallyas available directly from the company or from a commercial dataprovider of the type described herein above, lithe model is accurate(step 260), then customer reports based on the model are generated (step265) and distributed similar to buy- and sell-side analyst reports. Aswill be understood by the reader, acceptable accuracy is determined bythe operator of analytic system 102 based upon such factors as thevolatility of the metric being modeled and the tightness, or range, ofthe company's own guidance relating to the metric.

If the model proves inaccurate (step 260), then it is corrected (step270). With a first data set, the model may be corrected, for example, byone of several steps including but not limited to: i) further cleaningand validating of the data, ii) selection of a new analytical techniquefor evaluating the data and developing the model, and iii) others thatwill be apparent to the reader. For example, if the linear regressionmodel of the cleaned data is inaccurate, the data may be further cleanedto remove the inherent latency errors noted above, or a moresophisticated analytical model selected, for example a non-linearanalytical model. For purposes of continuing to describe the invention,it will be assumed that a linear regression analysis of the cleaned andvalidated data provides a model that appears to accurately predict theunit sales of Autostore.

The above-described model, while interesting, is based solely upon asingle data source. In accordance with a key feature of the presentinvention, multiple, non-fundamental and non-traditional data sources,each pertinent to the selected performance metric, are used incombination to analyze the performance of the metric and/or to develop amodel for the performance of the metric. This dual-sourcing, ortriangulating, of multiple data sources across different mediansfunctions to optimally model the selected company metric. It will beseen that the invention thus optimally proxies different factorsindividually, or in sub-aggregates, such that they may be combined intoa model with the most robust properties. As used herein, desirable,robust properties include correct treatment of outliers, minimumvariance in error terms, and other properties as will now be apparent tothe reader.

As an example illustrating the use of multiple data sets, supposing thatthe industry of interest is casino gaming and that one metric ofparticular interest is monthly revenue for a particular geographic area.In accordance with the invention, the metric is disjoined into threemain components of its volatility: 1) a pure growth rate, 2) seasonaleffects, and 3) effects due to casino traffic trends, including theinherent correlations with occupancy. Upon extracting these factors fromthe data via three separate analytical procedures ranging frommultivariate regression to a minimum volume ellipsoid estimationprocedure there is removed all inherent information leaving what isknown as white noise, effectively a randomly distributed noise with nodistinguishable pattern. There is thus extracted all relevantinformation in the data.

Continuing now with the Autostore example, it will be assumed thatinventory data for used car dealers is another data set the operator ofanalytical system 102 determines may be pertinent to the selected unitsales metric. It is then determined that used care inventory data isavailable for 9000+ used care dealers by license from an online used carlead generation service, for example Autobytel™ or Carpoint™. This datais identified as a second source of pertinent data (step 210) by theanalyst operating analytical system 102 and is licensed for use (step215) from the owner. In particular, it is determined to use monthlychange in inventory data in the geographies of Autostore stores as theinput data for the model to be developed.

This second set of data describing the inventory of the automobiledealerships is evaluated for viability (step 220) as described above andif viable (step 225) is used to support an analysis of Autostore's unitsales. In accordance with the present invention, because this is thesecond set of data (step 240) it is combined with the first set, orearlier sets if multiple sets exist, of data (step 245). That is, thenew data set is combined with the date and seller registration datadescribed above, to prepare the model of Autostore's unit sales (step250). As described above with respect to FIG. 4, the model is preparedusing a selected analytical technique whereby to generate a mathematicalmodel. To continue the above example, it will be assumed that thecorrelation between the combined data set of i) monthly inventory changedata for large numbers of dealerships, and ii) registration data, withunit sales by Autostore, is best determined by a complex modelingtechnique, for example a neural network analysis applied to the combineddata set.

This second model, supplemented with the new data set and analysisthereof, is compared to the actual performance of Autostore. As notedabove, such comparison can be against the historical, current and/orfuture performance of the company (step 255). In accordance with afeature of the present invention, the expected result is that two ormore relevant data sets will provide more accurate results than a singledata set. Again, if the analysis is accurate it is used to generate areport (step 265) for consumers. Otherwise, the model is corrected (step270). As described above, correction may be made in various ways,including: i) re-cleaning and re-validating one or more data sets, ii)selecting a different mathematical analysis technique, iii) replacingone or more of the data sets with different data, iv) changing therelative weighting of the different data sets, and v) other techniquesthat will now be apparent to the reader.

It will be appreciated by the reader that additional data sets may beidentified, collected and used to develop the model of the selectedperformance metric. Further, each new data set may optionally be modeled(step 250) and validated (step 255) against actual company performance,as described with respect to the first set of registration data, priorto its combination with other data sets. It will be understood thatwhile such individual modeling and validating of a data set may beinteresting, it is not determinative of how each data set is likely to,in combination with other data sets, improve the model.

As noted above, another source of data comprises the collection of datafrom publicly available sources such as the Internet. Such a process isknown variously in the industry as “deep searching”, “indexing”, orusing an “agent” to “extract” information from sites. More specifically,this data collection entails mapping unstructured or semi-structureddata, collected in human readable formats such as HTML from web sites,into structured machine readable formats in a database. This homogenizedformat is then accessible to standard analysis tools. Furthermore, itprovides a ‘snapshot’ of the information on the site at the point intime that the data was captured. This enables an analyst to observetrends in the collected data that occur over time. Generally, datacollected in this manner must be cleaned and calibrated, just like theother data sources described herein.

It will be understood that automated data gathering is not limited toHTML, web sites, or even the Internet. For example, in addition to HTML,information may sometimes be accessible in XML, plain text, and CSV orother formats. The original format is typically human readable, such asHTML or plain text, but on occasion may be in a structured or semistructured format, such as XML or CSV. In addition to web sites or webservers, information may be collected from other types of sites,including “FTP” servers, email servers, instant messaging servers.Furthermore, other mediums, such as the public phone system and wirelessnetworks, can serve as pathways for data gathering, independent of theInternet.

Other available sources of data include the use of panels, electronicsurveillance, and online surveys. It will be understood that a panel isthe online equivalent of a Nielsen family, excepting that instead ofchannels and TV programs, the medium consists of web sites and webpages. Electronic surveillance refers to the use of an electronicdevice, such as a computer with a camera attached, to count detectableitems such as people and cars. Online surveys constitute a wide spreadpractice well known to the reader.

Continuing the ongoing example, it is assumed that the new model isaccurate within analyst needs. In accordance with the present invention,there is thus provided an accurate model for Autostore's unit salesusing two differing data sets cleaned, validated, combined and analyzedwith appropriate mathematical analytical tools. In comparison to thelimited sources of data and/or the subjective and anecdotal informationprovided by traditional analysts, the model of the present invention isbased upon non-fundamental, nontraditional but objective data andrepeatable, provable mathematical analysis.

For purposes of illustration and without limitation, there are providedbelow other examples of selected companies, performance metrics anddatasets used to analyze those performance metrics:

EXAMPLE 1

-   -   Company=an online DVD rental service    -   Performance metric=net new subscribers    -   Data set I: a large panel of Internet browsers who permit        monitoring of their activities in exchange for a fee    -   Data set II: a survey of current rental service subscribers

EXAMPLE 2

-   -   Company=an online travel service    -   Performance metric=gross domestic bookings    -   Data set I: a large panel of Internet browsers who permit        monitoring of their activities in exchange for a fee    -   Data set II: a commercially available source of processed,        anonymized credit-card transaction data

EXAMPLE 3

-   -   Company=an Internet auction site    -   Performance metric=Quarterly new item listings by country    -   Data set I: automated, online monitoring of reported new        listings    -   Data set II: automated, online monitoring of actual auctions on        the auction site

In addition to analyzing individual company performance metrics, thepresent invention can be used to analyze the performance of entireselected industries, identifying trends, inflection points, strategychanges, performance shifts, etc, by selecting and processingappropriate industry-relevant data in accordance with the teachingsherein. It will also be understood that, in addition to cleaned rawdata, in one embodiment of the invention processed model data is used asinput to the process, thereby using first- or lower-order model data todevelop second- or higher-order models.

There have thus been provided new and improved methods and systems formodeling the performance of selected company metrics. The invention usesmultiple, non-traditional sets of objective data along with mathematicalanalytical techniques to yield models providing transparency andvisibility into company performance relating to the particular metrics.The invention is useful in many different respects, for example toidentify company inflection points and changes in strategy. Theinvention may be applied to analyze the performance of a company and/orthe performance of a selected industry or industry sector.

While the invention has been described with respect to particularembodiments, it is not thus limited. Numerous modifications, changes andenhancements within the scope of the invention will now be apparent tothe reader.

1. A method for preparing a model to analyze a performance metric of aselected company, comprising: identifying a first data source having afirst set of non-fundamental data pertinent to the performance metric ofthe selected company; collecting the first set of data; identifying anadditional data source, the additional data source having an additionalset of non-fundamental data different from the first set of data andpertinent to the performance metric of the selected company; collectingthe additional set of data; combining the first set of data and theadditional set of data in a combined data set; selecting an analyticalprocess to apply to the combined data set; applying the selectedanalytical process to the combined data set to develop a model of theperformance metric; and validating the model of the performance metricagainst the actual performance of the selected company.
 2. A methodoperable on a computer for preparing a model to analyze a performancemetric of a selected company, comprising: a) identifying a first datasource having a first set of data pertinent to the performance metric ofthe selected company; b) collecting into the computer the first set ofdata; c) validating the first set of data; d) identifying an additionaldata source, the additional data source having an additional set of datadifferent from the first set of data and pertinent to the performancemetric of the selected company; e) collecting into the computer theadditional set of data; f) validating the additional set of data; g)combining the first set of data and the additional set of data in acombined data set; h) selecting an analytical process to apply to thecombined data set; i) applying the selected analytical process to thecombined data set to develop a model of the performance metric; and j)validating the model of the performance metric against the actualperformance of the selected company.
 3. The method of claim 2 andfurther comprising the step of, for at least a selected one of the firstdata set and the additional data set, prior to step g), performing thesteps of: selecting an analytical process to apply to the selected dataset; applying the selected analytical process to the selected data setto develop a model of the performance metric; and validating the modelof the performance metric against the actual performance of the selectedcompany.
 4. The method of claim 3 wherein, if the step of validating themodel of the performance metric against the actual performance of theselected company fails then discarding the selected data set andperforming steps d), e), f), g), h), i) and j) for an additional dataset.
 5. The method of claim 2 and further including the step ofrepeating steps d), e), f), g), h), i) and j) for at least oneadditional data set.
 6. The method of claim 5 wherein at least one ofthe first data set and additional data sets are licensed from athird-party.
 7. The method of claim 5 wherein at least one of the firstdata set and additional data sets are proprietary to an operator of thecomputer.
 8. The method of claim 5 wherein the at least one proprietarydata set is collected from publicly available data by an operator of thecomputer.
 9. The method of claim 5 wherein each of the first data setand the additional data set comprise non-fundamental company data. 10.The method of claim 2 wherein the steps c) and f) of validating each ofthe first set of data and the additional set of data includes the stepsof: cleaning the data to remove extraneous data; and evaluating thevalidity of the cleaned data.
 11. The method of claim 2 wherein theselected analytical process is selected from the group comprising aregression analysis, a neural network analysis and a spectral analysis.12. The method of claim 2 wherein at least one of the first data set andthe additional data set comprises data output from a model of theperformance metric.
 13. The method of claim 2 wherein at least one ofthe first data set and the additional data set comprise data collectedfrom a publicly accessible Internet web site. 14-29. (canceled)